A Cloud-Based Framework for Machine Learning Workloads and Applications

A Cloud-Based Framework for Machine Learning Workloads and Applications

In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing...

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Bibliographic Details
Main Authors: Alvaro Lopez Garcia, Jesus Marco De Lucas, Marica Antonacci, Wolfgang Zu Castell, Mario David, Marcus Hardt, Lara Lloret Iglesias, Germen Molto, Marcin Plociennik, Viet Tran, Andy S. Alic, Miguel Caballer, Isabel Campos Plasencia, Alessandro Costantini, Stefan Dlugolinsky, Doina Cristina Duma, Giacinto Donvito, Jorge Gomes, Ignacio Heredia Cacha, Keiichi Ito, Valentin Y. Kozlov, Giang Nguyen, Pablo Orviz Fernandez, Zdenek Sustr, Pawel Wolniewicz
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Cloud computing
computers and information processing
deep learning
distributed computing
machine learning
serverless architectures
Online Access:https://ieeexplore.ieee.org/document/8950411/

Internet

https://ieeexplore.ieee.org/document/8950411/

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